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dc.contributor.authorNascimento, J.-
dc.contributor.authorCarneiro, G.-
dc.identifier.citationProceedings / IEEE International Symposium on Biomedical Imaging: from nano to macro. IEEE International Symposium on Biomedical Imaging, 2016, vol.2016-June, pp.867-871-
dc.description.abstractThis paper proposes a novel combination of manifold learning with deep belief networks for the detection and segmentation of left ventricle (LV) in 2D - ultrasound (US) images. The main goal is to reduce both training and inference complexities while maintaining the segmentation accuracy of machine learning based methods for non-rigid segmentation methodologies. The manifold learning approach used can be viewed as an atlas-based segmentation. It partitions the data into several patches. Each patch proposes a segmentation of the LV that somehow must be fused. This is accomplished by a deep belief network (DBN) multi-classifier that assigns a weight for each patch LV segmentation. The approach is thus threefold: (i) it does not rely on a single segmentation, (ii) it provides a great reduction in the rigid detection phase that is performed at lower dimensional space comparing with the initial contour space, and (iii) DBN's allows for a training process that can produce robust appearance models without the need of large annotated training sets.-
dc.description.statementofresponsibilityJacinto C. Nascimento, Gustavo Carneiro-
dc.relation.ispartofseriesIEEE International Symposium on Biomedical Imaging-
dc.rights© 2016 IEEE-
dc.subjectManifolds, training, image segmentation, visualization, principal component analysis, complexity theory, context-
dc.titleMulti-atlas segmentation using manifold learning with deep belief networks-
dc.typeConference paper-
dc.contributor.conference2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI 2016) (13 Apr 2016 - 16 Apr 2016 : Prague, Czech Republic)-
dc.identifier.orcidCarneiro, G. [0000-0002-5571-6220]-
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